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Threadvest is a platform that provides simple yet powerful insights into the world of ETF investing through institutional data processing and article content. Their ETF Miner allows users to search through thousands of US-issued ETFs based on their interest and priorities and then links to brokerages so users can purchase those funds directly.
Threadvest supports more than 2,000 US-issued ETFs through its web architecture. Each of the ETFs has dozens of data fields describing the ETF, its performance, mission, and other various parameters. We wanted to make the process of finding an ETF simple, therefore we came up with ETF Miner. ETF Miner is a web-based architecture which allows users to scan through ETFs based on their interests, priorities, and goals. The main challenge was to find a partner with enough data to power our methodology that processes all the relevant ETF metrics.
After looking at several vendors we decided to partner with Intrinio data to power our ETF Miner. Currently, we receive and process a large amount of data each day. The use of Intrinio and their First Bridge ETF data for us is threefold.
- ETF Characteristics – we selected a few fields that we find most important when an investor is looking at an ETF investment. We try to keep it simple and to the point without crowding our page with nice to have data points.
- ETF Miner User Priorities – each user is asked to align four priorities: Fee, Tenure, Size, and Spread. These are four concepts that we think every user looking to invest should know about and should have an opinion which one is more important than the other one.
- ETF Miner Proprietary Analysis – this is the step that consumes that most of Intrinio data. We do ask investors to rank priorities, however, we also want to make sure we don’t miss other important aspects of the ETF as an investment. Some of these concepts are more complicated and asking a user to have an opinion would mean that we assume all our users are experts at the topic which does not have to be true. Also, some of the considerations are universally agreed upon to be important and calculations on this happen behind the scene. For example, if there are two similar ETFs, but one is more concentrated than the other, our model would favor the one that is more diversified. Other aspects that our ETF Proprietary Analysis takes into account are historical vs current volatility, legal structure, etc.
ETF Miner has been live for about a month and a half. Our development queue still runs long, and we are always tweaking and trying to improve to deliver the best experience to our users. For the short amount of time we have been around, the Miner has been used several hundred times, and we have been able to find a few hundred ETFs for our users. Intrinio has been a great partner throughout our journey. The responsiveness of the team and the quick turnaround made it a pleasure to work together. The data we receive is so comprehensive that now we are probably using roughly 35% of it. As our product grows and the development work continues, we are looking to expand and start using more of the data we get. In the same time, we would like to stay true to our mission where we provide our users with only the most relevant data and we keep the big data processing behind the scenes.